Location, location, location: a discrete choice experiment to inform COVID-19 vaccination programme delivery in the UK.

BACKGROUND: Large-scale vaccination is fundamental to combatting COVID-19. In March 2021, the UK's vaccination programme had delivered vaccines to large proportions of older and more vulnerable population groups; however, there was concern that uptake would be lower among young people. This research was designed to elicit the preferences of 18-29-year-olds regarding key delivery characteristics and assess the influence of these on intentions to get vaccinated, to inform planning for this cohort. METHODS: From 25 March to 2 April 2021, an online sample of 2012 UK adults aged 18-29 years participated in a Discrete Choice Experiment. Participants made six choices, each involving two SMS invitations to book a vaccination appointment and an opt-out. Invitations had four attributes (1 × 5 levels, 3 × 3 levels): delivery mode, appointment timing, proximity, and sender. These were systematically varied according to a d-optimal design. Responses were analysed using a mixed logit model. RESULTS: The main effects logit model revealed a large alternative-specific constant (ß = 1.385, SE = 0.067, p < 0.001), indicating a strong preference for 'opting in' to appointment invitations. Pharmacies were dispreferred to the local vaccination centre (ß = - 0.256, SE = 0.072, p < 0.001), appointments in locations that were 30-45 min travel time from one's premises were dispreferred to locations that were less than 15 min away (ß = - 0.408, SE = 0.054, p < 0.001), and, compared to invitations from the NHS, SMSs forwarded by 'a friend' were dispreferred (ß = - 0.615, SE = 0.056, p < 0.001) but invitations from the General Practitioner were preferred (ß = 0.105, SE = 0.048, p = 0.028). CONCLUSIONS: The results indicated that the existing configuration of the UK's vaccination programme was well-placed to deliver vaccines to 18-29-year-olds; however, some adjustments might enhance acceptance. Local pharmacies were not preferred; long travel times were a disincentive but close proximity (0-15 min from one's premises) was not necessary; and either the 'NHS' or 'Your GP' would serve as adequate invitation sources. This research informed COVID-19 policy in the UK, and contributes to a wider body of Discrete Choice Experiment evidence on citizens' preferences, requirements and predicted behaviours regarding COVID-19.

Infection Control Behavior at Home During the COVID-19 Pandemic: Observational Study of a Web-Based Behavioral Intervention (Germ Defence).

BACKGROUND: To control the COVID-19 pandemic, people should adopt protective behaviors at home (self-isolation, social distancing, putting shopping and packages aside, wearing face coverings, cleaning and disinfecting, and handwashing). There is currently limited support to help individuals conduct these behaviors. OBJECTIVE: This study aims to report current household infection control behaviors in the United Kingdom and examine how they might be improved. METHODS: This was a pragmatic cross-sectional observational study of anonymous participant data from Germ Defence between May 6-24, 2020. Germ Defence is an open-access fully automated website providing behavioral advice for infection control within households. A total of 28,285 users sought advice from four website pathways based on household status (advice to protect themselves generally, to protect others if the user was showing symptoms, to protect themselves if household members were showing symptoms, and to protect a household member who is at high risk). Users reported current infection control behaviors within the home and intentions to change these behaviors. RESULTS: Current behaviors varied across all infection control measures but were between sometimes (face covering: mean 1.61, SD 1.19; social distancing: mean 2.40, SD 1.22; isolating: mean 2.78, SD 1.29; putting packages and shopping aside: mean 2.75, SD 1.55) and quite often (cleaning and disinfecting: mean 3.17, SD 1.18), except for handwashing (very often: mean 4.00, SD 1.03). Behaviors were similar regardless of the website pathway used. After using Germ Defence, users recorded intentions to improve infection control behavior across all website pathways and for all behaviors (overall average infection control score mean difference 0.30, 95% CI 0.29-0.31). CONCLUSIONS: Self-reported infection control behaviors other than handwashing are lower than is optimal for infection prevention, although handwashing is much higher. Advice using behavior change techniques in Germ Defence led to intentions to improve these behaviors. Promoting Germ Defence within national and local public health and primary care guidance could reduce COVID-19 transmission.

Perceived risk factors for severe Covid-19 symptoms and their association with health behaviours: Findings from the HEBECO study.

Risk perceptions are important influences on health behaviours. We used descriptive statistics and multivariable logistic regression models to assess cross-sectionally risk perceptions for severe Covid-19 symptoms and their health behaviour correlates among 2206 UK adults from the HEBECO study. The great majority (89-99%) classified age 70+, having comorbidities, being a key worker, overweight, and from an ethnic minority as increasing the risk. People were less sure about alcohol drinking, vaping, and nicotine replacement therapy use (17.4-29.5% responding 'don't know'). Relative to those who did not, those who engaged in the following behaviours had higher odds of classifying these behaviours as (i) decreasing the risk: smoking cigarettes (adjusted odds ratios, aORs, 95% CI = 2.26, 1.39-3.37), and using e-cigarettes (aORs = 5.80, 3.25-10.34); (ii) having no impact: smoking cigarettes (1.98; 1.42-2.76), using e-cigarettes (aORs = 2.63, 1.96-3.50), drinking alcohol (aORs = 1.75, 1.31-2.33); and lower odds of classifying these as increasing the risk: smoking cigarettes (aORs: 0.43, 0.32-0.56), using e-cigarettes (aORs = 0.25, 0.18-0.35). Similarly, eating more fruit and vegetables was associated with classifying unhealthy diet as 'increasing risk' (aOR = 1.37, 1.12-1.69), and exercising more with classifying regular physical activity as 'decreasing risk' (aOR = 2.42, 1.75-3.34). Risk perceptions for severe Covid-19 among UK adults were lower for their own health behaviours, evidencing optimism bias. These risk perceptions may form barriers to changing people's own unhealthy behaviours, make them less responsive to interventions that refer to the risk of Covid-19 as a motivating factor, and exacerbate inequalities in health behaviours and outcomes.

Effectiveness of digital interventions to improve household and community infection prevention and control behaviours and to reduce incidence of respiratory and/or gastro-intestinal infections: a rapid systematic review.

BACKGROUND: Digital interventions have potential to efficiently support improved hygiene practices to reduce transmission of COVID-19. OBJECTIVE: To evaluate the evidence for digital interventions to improve hygiene practices within the community. METHODS: We reviewed articles published between 01 January 2000 and 26 May 2019 that presented a controlled trial of a digital intervention to improve hygiene behaviours in the community. We searched MEDLINE, Embase, PsycINFO, Cochrane Controlled Register of Trials (CENTRAL), China National Knowledge Infrastructure and grey literature. Trials in hospitals were excluded, as were trials aiming at prevention of sexually transmitted infections; only target diseases with transmission mechanisms similar to COVID-19 (e.g. respiratory and gastrointestinal infections) were included. Trials had to evaluate a uniquely digital component of an intervention. Study designs were limited to randomised controlled trials, controlled before-and-after trials, and interrupted time series analyses. Outcomes could be either incidence of infections or change in hygiene behaviours. The Risk of Bias 2 tool was used to assess study quality. RESULTS: We found seven studies that met the inclusion criteria. Six studies reported successfully improving self-reported hygiene behaviour or health outcomes, but only one of these six trials, Germ Defence, confirmed improvements using objective measures (reduced consultations and antibiotic prescriptions). Settings included kindergartens, workplaces, and service station restrooms. Modes of delivery were diverse: WeChat, website, text messages, audio messages to mobiles, electronic billboards, and electronic personal care records. Four interventions targeted parents of young children with educational materials. Two targeted the general population; these also used behaviour change techniques or theory to inform the intervention. Only one trial had low risk of bias, Germ Defence; the most common concerns were lack of information about the randomisation, possible bias in reporting of behavioural outcomes, and lack of an analysis plan and possible selective reporting of results. CONCLUSION: There was only one trial that was judged to be at low risk of bias, Germ Defence, which reduced incidence and severity of illness, as confirmed by objective measures. Further evaluation is required to determine the effectiveness of the other interventions reviewed. TRIAL REGISTRATION: PROSPERO CRD42020189919 .

Adapting Behavioral Interventions for a Changing Public Health Context: A Worked Example of Implementing a Digital Intervention During a Global Pandemic Using Rapid Optimisation Methods.

Background: A rigorous approach is needed to inform rapid adaptation and optimisation of behavioral interventions in evolving public health contexts, such as the Covid-19 pandemic. This helps ensure that interventions are relevant, persuasive, and feasible while remaining evidence-based. This paper provides a set of iterative methods to rapidly adapt and optimize an intervention during implementation. These methods are demonstrated through the example of optimizing an effective online handwashing intervention called Germ Defense. Methods: Three revised versions of the intervention were rapidly optimized and launched within short timeframes of 1-2 months. Optimisations were informed by: regular stakeholder engagement; emerging scientific evidence, and changing government guidance; rapid qualitative research (telephone think-aloud interviews and open-text surveys), and analyses of usage data. All feedback was rapidly collated, using the Table of Changes method from the Person-Based Approach to prioritize potential optimisations in terms of their likely impact on behavior change. Written feedback from stakeholders on each new iteration of the intervention also informed specific optimisations of the content. Results: Working closely with clinical stakeholders ensured that the intervention was clinically accurate, for example, confirming that information about transmission and exposure was consistent with evidence. Patient and Public Involvement (PPI) contributors identified important clarifications to intervention content, such as whether Covid-19 can be transmitted via air as well as surfaces, and ensured that information about difficult behaviors (such as self-isolation) was supportive and feasible. Iterative updates were made in line with emerging evidence, including changes to the information about face-coverings and opening windows. Qualitative research provided insights into barriers to engaging with the intervention and target behaviors, with open-text surveys providing a useful supplement to detailed think-aloud interviews. Usage data helped identify common points of disengagement, which guided decisions about optimisations. The Table of Changes was modified to facilitate rapid collation and prioritization of multiple sources of feedback to inform optimisations. Engagement with PPI informed the optimisation process. Conclusions: Rapid optimisation methods of this kind may in future be used to help improve the speed and efficiency of adaptation, optimization, and implementation of interventions, in line with calls for more rapid, pragmatic health research methods.

A randomised experiment of health, cost and social norm message frames to encourage acceptance of swaps in a simulation online supermarket.

Offering lower-energy food swaps to customers of online supermarkets could help to decrease energy (kcal) purchased and consumed. However, acceptance rates of such food swaps tend to be low. This study aimed to see whether framing lower-energy food swaps in terms of cost savings or social norms could improve likelihood of acceptance relative to framing swaps in terms of health benefits. Participants (n = 900) were asked to shop from a 12-item shopping list in a simulation online supermarket. When a target high-energy food was identified in the shopping basket at check-out, one or two lower-energy foods would be suggested as an alternative (a "swap"). Participants were randomised to only see messages emphasising health benefits (fewer calories), cost benefits (lower price) or social norms (others preferred this product). Data were analysed for 713 participants after exclusions. Participants were offered a mean of 3.17 swaps (SD = 1.50), and 12.91% of swaps were accepted (health = 14.31%, cost = 11.49%, social norms = 13.18%). Swap acceptance was not influenced by the specific swap frame used (all p > .170). Age was significantly and positively associated with swap acceptance (b = 0.02, SE = 0.00, p < .001), but was also associated with smaller decreases in energy change (b = 0.46, SE = .19, p = .014). Overall, offering swaps reduced both energy (kcal) per product (b = -9.69, SE = 4.07, p = .017) and energy (kcal) per shopping basket (t712 = 11.09, p < .001) from pre- to post-intervention. Offering lower-energy food swaps could be a successful strategy for reducing energy purchased by customers of online supermarkets. Future research should explore alternative solutions for increasing acceptance rates of such swaps.